{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "print(ax)\n",
    "# 绘制一个余弦曲线\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "line, = ax.plot(t, s, lw=2)\n",
    "\n",
    "# 绘制一个黑色，两端缩进的箭头\n",
    "ax.annotate('local max', xy=(2, 1), xytext=(4, 1.5),\n",
    "            xycoords='data',\n",
    "            arrowprops=dict()\n",
    "            )\n",
    "ax.set_ylim(-2, 2)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}